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Many authors have made contributions in the development of duality theory for multiple objective programming problems.
Taguchi method is a powerful optimization tool designed for the optimization of single performance characteristics, but most of the real engineering problems demands for multiple objective optimization.
However, for multiple objective problems, explicit local search strategies are relatively under studied, compared to other aspects of the search process.
One of the major step is taken by Liu et al. [14] in the direction of obtaining sufficient optimality conditions for multiple objective programming problem and multiobjective fractional programming problem involving ( H p, r ) -invex functions.
Process System Engineering (PSE) techniques for multiple objective optimisation have to date focused typically on the optimisation of cost versus the potential for waste minimisation, with the recent inclusion of operability issues.
This paper details the development and initial validation of EEPFD through two identified needs of early stage design: 1) the ability to accommodate formal variety and varying degrees of geometric complexity; and 2) the ability to provide improved performance feedback for multiple objective functions.
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For multiple objectives continuous space optimization problem, Zhan et al. [16] proposed a co-evolutionary technique for multiple objectives optimization problem.
An optimization problem for multiple objectives is called a multi-objective optimization problem.
However, it is not possible to address optimal solutions for multiple objectives in a spatial context.
Due to the high investment cost, batteries should be designed for multiple objectives to increase their economic feasibility.
In this paper we present an adaptation of a single-objective Tabu Search algorithm for multiple objectives.
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